Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [25]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [26]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[26]:
<matplotlib.image.AxesImage at 0x7f8b65be9e10>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [27]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[27]:
<matplotlib.image.AxesImage at 0x7f8b657cdd68>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [28]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [29]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [30]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):        
        alpha = 0.1          
        
        h1 = tf.layers.conv2d(inputs=images,
                              filters=64,
                              kernel_size=5,
                              kernel_initializer = tf.random_normal_initializer(stddev=0.02),
                              strides=2,
                              padding='same')
        h1 = tf.nn.dropout(h1, 0.3)
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(inputs=h1,
                              filters=128,
                              kernel_size=5,
                              kernel_initializer = tf.random_normal_initializer(stddev=0.02),
                              strides=2,
                              padding='same')
        h2 = tf.nn.dropout(h2, 0.3)
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(inputs=h2,
                              filters=256,
                              kernel_size=5,
                              kernel_initializer = tf.random_normal_initializer(stddev=0.02),
                              strides=2,
                              padding='same')
        h3 = tf.nn.dropout(h3, 0.3)
        h3 = tf.layers.batch_normalization(inputs=h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        flat = tf.reshape(h3, (-1, 4 * 4 * 512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [45]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        c1 = tf.layers.dense(z, 2 * 2 * 512)
        
        c1 = tf.reshape(c1, (-1, 2, 2, 512))
        batch_norm1 = tf.layers.batch_normalization(c1, training=is_train)
        relu1 = tf.maximum(alpha * c1, c1)
        
        c2 = tf.layers.conv2d_transpose(inputs=c1,
                                        filters=256,
                                        kernel_size=3,
                                        strides=2,
                                        padding='same')
        batch_norm2 = tf.layers.batch_normalization(c2, training=is_train)
        relu2 = tf.maximum(alpha * c2, c2)
        
        c3 = tf.layers.conv2d_transpose(inputs=c2,
                                        filters=128,
                                        kernel_size=4,
                                        strides=1,
                                        padding='valid')
        batch_norm3 = tf.layers.batch_normalization(c3, training=is_train)
        relu3 = tf.maximum(alpha * c3, c3)
        
        c4 = tf.layers.conv2d_transpose(inputs=c3,
                                        filters=64,
                                        kernel_size=3,
                                        strides=2,
                                        padding='same')
        batch_norm4 = tf.layers.batch_normalization(c4, training=is_train)
        relu4 = tf.maximum(alpha * c4, c4)
        
        logits = tf.layers.conv2d_transpose(c4, out_channel_dim, 5, strides=2, padding='same')
        
        output = tf.tanh(logits)
    
    return output      


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [46]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                labels=tf.ones_like(d_logits_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                labels=tf.ones_like(d_logits_fake)))
    
    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [47]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    v_train = tf.trainable_variables()
    v_disc = [v for v in v_train if v.name.startswith('discriminator')]
    v_gen = [v for v in v_train if v.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    d_updates = [opt for opt in update_ops if opt.name.startswith('discriminator')]
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]

    with tf.control_dependencies(d_updates):
        opt_disc = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=v_disc)

    with tf.control_dependencies(g_updates):
        opt_gen = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=v_gen)
            
    return opt_disc, opt_gen


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [48]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [49]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=10, show_every=20):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, l_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real=input_real, input_z=input_z, out_channel_dim=data_shape[3])    
    d_opt, g_opt = model_opt(d_loss=d_loss, g_loss=g_loss, learning_rate=learning_rate, beta1=beta1)
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2.0
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [50]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.8417... Generator Loss: 1.2054
Epoch 1/2... Discriminator Loss: 0.4294... Generator Loss: 1.7555
Epoch 1/2... Discriminator Loss: 0.2154... Generator Loss: 2.6409
Epoch 1/2... Discriminator Loss: 0.2027... Generator Loss: 2.8061
Epoch 1/2... Discriminator Loss: 0.1866... Generator Loss: 3.1009
Epoch 1/2... Discriminator Loss: 0.2069... Generator Loss: 2.8833
Epoch 1/2... Discriminator Loss: 0.3271... Generator Loss: 2.6000
Epoch 1/2... Discriminator Loss: 0.2629... Generator Loss: 2.3445
Epoch 1/2... Discriminator Loss: 0.4589... Generator Loss: 2.8437
Epoch 1/2... Discriminator Loss: 0.4866... Generator Loss: 1.6380
Epoch 1/2... Discriminator Loss: 0.4740... Generator Loss: 2.6011
Epoch 1/2... Discriminator Loss: 0.5368... Generator Loss: 1.4406
Epoch 1/2... Discriminator Loss: 0.4590... Generator Loss: 3.1582
Epoch 1/2... Discriminator Loss: 0.3819... Generator Loss: 2.0523
Epoch 1/2... Discriminator Loss: 0.3926... Generator Loss: 2.6995
Epoch 1/2... Discriminator Loss: 0.3447... Generator Loss: 2.5164
Epoch 1/2... Discriminator Loss: 0.4546... Generator Loss: 2.9015
Epoch 1/2... Discriminator Loss: 0.4073... Generator Loss: 2.2160
Epoch 1/2... Discriminator Loss: 0.3567... Generator Loss: 2.8809
Epoch 1/2... Discriminator Loss: 0.1777... Generator Loss: 3.3923
Epoch 1/2... Discriminator Loss: 0.2246... Generator Loss: 2.6667
Epoch 1/2... Discriminator Loss: 0.3575... Generator Loss: 3.0722
Epoch 1/2... Discriminator Loss: 0.3114... Generator Loss: 2.4407
Epoch 1/2... Discriminator Loss: 1.0056... Generator Loss: 0.7565
Epoch 1/2... Discriminator Loss: 0.7683... Generator Loss: 1.2714
Epoch 1/2... Discriminator Loss: 0.3766... Generator Loss: 3.0189
Epoch 1/2... Discriminator Loss: 0.2890... Generator Loss: 2.4244
Epoch 1/2... Discriminator Loss: 0.4641... Generator Loss: 2.5584
Epoch 1/2... Discriminator Loss: 0.3834... Generator Loss: 2.0553
Epoch 1/2... Discriminator Loss: 0.4613... Generator Loss: 1.8351
Epoch 1/2... Discriminator Loss: 0.2533... Generator Loss: 3.3989
Epoch 1/2... Discriminator Loss: 0.1203... Generator Loss: 3.3884
Epoch 1/2... Discriminator Loss: 0.4366... Generator Loss: 1.6834
Epoch 1/2... Discriminator Loss: 0.8659... Generator Loss: 3.2800
Epoch 1/2... Discriminator Loss: 0.3538... Generator Loss: 3.4884
Epoch 1/2... Discriminator Loss: 0.5426... Generator Loss: 3.7643
Epoch 1/2... Discriminator Loss: 0.4029... Generator Loss: 2.6479
Epoch 1/2... Discriminator Loss: 0.3778... Generator Loss: 2.3331
Epoch 1/2... Discriminator Loss: 0.3026... Generator Loss: 3.0273
Epoch 1/2... Discriminator Loss: 0.2966... Generator Loss: 2.7559
Epoch 1/2... Discriminator Loss: 0.1578... Generator Loss: 3.6176
Epoch 1/2... Discriminator Loss: 0.2539... Generator Loss: 4.4607
Epoch 1/2... Discriminator Loss: 0.5641... Generator Loss: 2.6244
Epoch 1/2... Discriminator Loss: 0.4813... Generator Loss: 2.4009
Epoch 1/2... Discriminator Loss: 0.4757... Generator Loss: 2.0539
Epoch 1/2... Discriminator Loss: 0.6601... Generator Loss: 1.8290
Epoch 1/2... Discriminator Loss: 0.9019... Generator Loss: 2.5171
Epoch 1/2... Discriminator Loss: 0.5331... Generator Loss: 1.3519
Epoch 1/2... Discriminator Loss: 0.3633... Generator Loss: 2.7875
Epoch 1/2... Discriminator Loss: 0.5380... Generator Loss: 1.7833
Epoch 1/2... Discriminator Loss: 0.3045... Generator Loss: 2.1434
Epoch 1/2... Discriminator Loss: 0.3294... Generator Loss: 2.9245
Epoch 1/2... Discriminator Loss: 0.4271... Generator Loss: 2.7669
Epoch 1/2... Discriminator Loss: 0.3385... Generator Loss: 2.0831
Epoch 1/2... Discriminator Loss: 0.6996... Generator Loss: 1.9984
Epoch 1/2... Discriminator Loss: 1.0815... Generator Loss: 2.5705
Epoch 1/2... Discriminator Loss: 0.4824... Generator Loss: 3.3913
Epoch 1/2... Discriminator Loss: 0.5496... Generator Loss: 1.1051
Epoch 1/2... Discriminator Loss: 0.6917... Generator Loss: 1.5665
Epoch 1/2... Discriminator Loss: 0.2949... Generator Loss: 2.1549
Epoch 1/2... Discriminator Loss: 0.3904... Generator Loss: 2.7847
Epoch 1/2... Discriminator Loss: 1.5899... Generator Loss: 0.5127
Epoch 1/2... Discriminator Loss: 0.2502... Generator Loss: 3.1213
Epoch 1/2... Discriminator Loss: 0.5574... Generator Loss: 2.9144
Epoch 1/2... Discriminator Loss: 1.5790... Generator Loss: 0.7031
Epoch 1/2... Discriminator Loss: 0.5810... Generator Loss: 1.7032
Epoch 1/2... Discriminator Loss: 0.6323... Generator Loss: 2.5925
Epoch 1/2... Discriminator Loss: 2.1920... Generator Loss: 0.3278
Epoch 1/2... Discriminator Loss: 0.8030... Generator Loss: 0.9917
Epoch 1/2... Discriminator Loss: 0.7849... Generator Loss: 1.2154
Epoch 1/2... Discriminator Loss: 1.1536... Generator Loss: 1.6944
Epoch 1/2... Discriminator Loss: 1.2040... Generator Loss: 1.4443
Epoch 1/2... Discriminator Loss: 1.0696... Generator Loss: 1.0981
Epoch 1/2... Discriminator Loss: 0.9638... Generator Loss: 1.5939
Epoch 1/2... Discriminator Loss: 1.7079... Generator Loss: 0.7867
Epoch 1/2... Discriminator Loss: 0.8984... Generator Loss: 1.1036
Epoch 1/2... Discriminator Loss: 1.3963... Generator Loss: 0.9025
Epoch 1/2... Discriminator Loss: 1.4458... Generator Loss: 1.3450
Epoch 1/2... Discriminator Loss: 1.3083... Generator Loss: 0.7443
Epoch 1/2... Discriminator Loss: 1.0256... Generator Loss: 0.8694
Epoch 1/2... Discriminator Loss: 1.0683... Generator Loss: 1.1750
Epoch 1/2... Discriminator Loss: 1.0185... Generator Loss: 1.1027
Epoch 1/2... Discriminator Loss: 0.8950... Generator Loss: 2.0581
Epoch 1/2... Discriminator Loss: 0.7223... Generator Loss: 1.2901
Epoch 1/2... Discriminator Loss: 1.3945... Generator Loss: 0.8571
Epoch 1/2... Discriminator Loss: 0.8376... Generator Loss: 0.9268
Epoch 1/2... Discriminator Loss: 0.8409... Generator Loss: 1.4429
Epoch 1/2... Discriminator Loss: 1.0370... Generator Loss: 1.2799
Epoch 1/2... Discriminator Loss: 1.1040... Generator Loss: 1.2087
Epoch 1/2... Discriminator Loss: 0.7531... Generator Loss: 1.4506
Epoch 1/2... Discriminator Loss: 0.9020... Generator Loss: 1.9255
Epoch 1/2... Discriminator Loss: 0.8593... Generator Loss: 1.7926
Epoch 1/2... Discriminator Loss: 1.1730... Generator Loss: 0.9534
Epoch 2/2... Discriminator Loss: 0.6684... Generator Loss: 1.2037
Epoch 2/2... Discriminator Loss: 0.9551... Generator Loss: 1.0187
Epoch 2/2... Discriminator Loss: 0.4738... Generator Loss: 2.0119
Epoch 2/2... Discriminator Loss: 0.3933... Generator Loss: 2.5110
Epoch 2/2... Discriminator Loss: 0.9208... Generator Loss: 1.1577
Epoch 2/2... Discriminator Loss: 1.5072... Generator Loss: 0.8658
Epoch 2/2... Discriminator Loss: 1.0510... Generator Loss: 1.0559
Epoch 2/2... Discriminator Loss: 0.6606... Generator Loss: 1.6308
Epoch 2/2... Discriminator Loss: 0.6784... Generator Loss: 1.4226
Epoch 2/2... Discriminator Loss: 0.7732... Generator Loss: 1.0396
Epoch 2/2... Discriminator Loss: 0.7204... Generator Loss: 1.9213
Epoch 2/2... Discriminator Loss: 0.4359... Generator Loss: 2.1731
Epoch 2/2... Discriminator Loss: 1.2603... Generator Loss: 2.4358
Epoch 2/2... Discriminator Loss: 1.0274... Generator Loss: 0.9361
Epoch 2/2... Discriminator Loss: 0.7422... Generator Loss: 1.4576
Epoch 2/2... Discriminator Loss: 0.8443... Generator Loss: 2.7218
Epoch 2/2... Discriminator Loss: 1.3207... Generator Loss: 2.5523
Epoch 2/2... Discriminator Loss: 0.7186... Generator Loss: 2.0129
Epoch 2/2... Discriminator Loss: 1.8386... Generator Loss: 0.6481
Epoch 2/2... Discriminator Loss: 0.7292... Generator Loss: 2.0377
Epoch 2/2... Discriminator Loss: 0.7184... Generator Loss: 1.7395
Epoch 2/2... Discriminator Loss: 0.7358... Generator Loss: 1.9015
Epoch 2/2... Discriminator Loss: 1.2869... Generator Loss: 2.6551
Epoch 2/2... Discriminator Loss: 1.2324... Generator Loss: 0.8598
Epoch 2/2... Discriminator Loss: 0.7029... Generator Loss: 1.5103
Epoch 2/2... Discriminator Loss: 0.6587... Generator Loss: 2.1040
Epoch 2/2... Discriminator Loss: 0.9368... Generator Loss: 1.9862
Epoch 2/2... Discriminator Loss: 0.5754... Generator Loss: 1.8412
Epoch 2/2... Discriminator Loss: 0.6317... Generator Loss: 2.7730
Epoch 2/2... Discriminator Loss: 0.6417... Generator Loss: 1.8153
Epoch 2/2... Discriminator Loss: 1.2814... Generator Loss: 2.8889
Epoch 2/2... Discriminator Loss: 0.8987... Generator Loss: 1.2552
Epoch 2/2... Discriminator Loss: 0.6056... Generator Loss: 2.0696
Epoch 2/2... Discriminator Loss: 0.5856... Generator Loss: 2.9019
Epoch 2/2... Discriminator Loss: 0.5910... Generator Loss: 2.9227
Epoch 2/2... Discriminator Loss: 0.6360... Generator Loss: 2.0364
Epoch 2/2... Discriminator Loss: 2.6603... Generator Loss: 0.4967
Epoch 2/2... Discriminator Loss: 0.6789... Generator Loss: 1.4664
Epoch 2/2... Discriminator Loss: 0.5476... Generator Loss: 1.7564
Epoch 2/2... Discriminator Loss: 0.5832... Generator Loss: 1.9936
Epoch 2/2... Discriminator Loss: 0.9593... Generator Loss: 2.7787
Epoch 2/2... Discriminator Loss: 0.5486... Generator Loss: 3.3298
Epoch 2/2... Discriminator Loss: 1.8195... Generator Loss: 3.2945
Epoch 2/2... Discriminator Loss: 2.1954... Generator Loss: 0.4389
Epoch 2/2... Discriminator Loss: 0.5325... Generator Loss: 2.0875
Epoch 2/2... Discriminator Loss: 0.5908... Generator Loss: 1.7248
Epoch 2/2... Discriminator Loss: 0.5060... Generator Loss: 2.0674
Epoch 2/2... Discriminator Loss: 0.5557... Generator Loss: 1.9221
Epoch 2/2... Discriminator Loss: 0.3082... Generator Loss: 2.7408
Epoch 2/2... Discriminator Loss: 0.7501... Generator Loss: 2.1095
Epoch 2/2... Discriminator Loss: 0.4752... Generator Loss: 1.0061
Epoch 2/2... Discriminator Loss: 0.7627... Generator Loss: 0.8806
Epoch 2/2... Discriminator Loss: 0.5672... Generator Loss: 2.0577
Epoch 2/2... Discriminator Loss: 0.8413... Generator Loss: 2.1444
Epoch 2/2... Discriminator Loss: 0.4991... Generator Loss: 2.8831
Epoch 2/2... Discriminator Loss: 1.1226... Generator Loss: 2.2137
Epoch 2/2... Discriminator Loss: 0.3807... Generator Loss: 2.3339
Epoch 2/2... Discriminator Loss: 0.8620... Generator Loss: 1.4679
Epoch 2/2... Discriminator Loss: 0.3679... Generator Loss: 2.5379
Epoch 2/2... Discriminator Loss: 1.2452... Generator Loss: 1.2987
Epoch 2/2... Discriminator Loss: 0.4838... Generator Loss: 2.3657
Epoch 2/2... Discriminator Loss: 1.0402... Generator Loss: 1.7520
Epoch 2/2... Discriminator Loss: 0.3911... Generator Loss: 2.9281
Epoch 2/2... Discriminator Loss: 0.7536... Generator Loss: 1.3742
Epoch 2/2... Discriminator Loss: 0.5305... Generator Loss: 2.8413
Epoch 2/2... Discriminator Loss: 0.2397... Generator Loss: 3.5460
Epoch 2/2... Discriminator Loss: 0.7830... Generator Loss: 1.5491
Epoch 2/2... Discriminator Loss: 1.0569... Generator Loss: 3.1847
Epoch 2/2... Discriminator Loss: 0.7537... Generator Loss: 2.4220
Epoch 2/2... Discriminator Loss: 0.5927... Generator Loss: 1.5260
Epoch 2/2... Discriminator Loss: 0.5422... Generator Loss: 2.0702
Epoch 2/2... Discriminator Loss: 0.7284... Generator Loss: 2.0456
Epoch 2/2... Discriminator Loss: 0.4661... Generator Loss: 2.4218
Epoch 2/2... Discriminator Loss: 0.5183... Generator Loss: 2.5434
Epoch 2/2... Discriminator Loss: 0.4950... Generator Loss: 3.9450
Epoch 2/2... Discriminator Loss: 0.5227... Generator Loss: 1.5691
Epoch 2/2... Discriminator Loss: 0.6706... Generator Loss: 1.8802
Epoch 2/2... Discriminator Loss: 0.4237... Generator Loss: 3.8771
Epoch 2/2... Discriminator Loss: 0.3455... Generator Loss: 3.1479
Epoch 2/2... Discriminator Loss: 0.3498... Generator Loss: 1.8378
Epoch 2/2... Discriminator Loss: 0.6813... Generator Loss: 1.3663
Epoch 2/2... Discriminator Loss: 0.6202... Generator Loss: 3.1535
Epoch 2/2... Discriminator Loss: 0.4801... Generator Loss: 3.2774
Epoch 2/2... Discriminator Loss: 1.0702... Generator Loss: 1.7749
Epoch 2/2... Discriminator Loss: 0.3840... Generator Loss: 2.6046
Epoch 2/2... Discriminator Loss: 0.2402... Generator Loss: 2.8383
Epoch 2/2... Discriminator Loss: 0.4268... Generator Loss: 1.9802
Epoch 2/2... Discriminator Loss: 0.3905... Generator Loss: 2.8375
Epoch 2/2... Discriminator Loss: 0.2957... Generator Loss: 3.2544
Epoch 2/2... Discriminator Loss: 0.2927... Generator Loss: 2.2364
Epoch 2/2... Discriminator Loss: 0.3098... Generator Loss: 1.9691
Epoch 2/2... Discriminator Loss: 0.6978... Generator Loss: 2.7390
Epoch 2/2... Discriminator Loss: 0.9629... Generator Loss: 1.3980
Epoch 2/2... Discriminator Loss: 0.4565... Generator Loss: 2.1331

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [51]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0332... Generator Loss: 0.9904
Epoch 1/1... Discriminator Loss: 0.5919... Generator Loss: 1.9239
Epoch 1/1... Discriminator Loss: 0.3352... Generator Loss: 2.0487
Epoch 1/1... Discriminator Loss: 0.1949... Generator Loss: 2.6358
Epoch 1/1... Discriminator Loss: 0.1659... Generator Loss: 3.1007
Epoch 1/1... Discriminator Loss: 0.1267... Generator Loss: 3.2393
Epoch 1/1... Discriminator Loss: 0.1108... Generator Loss: 3.5417
Epoch 1/1... Discriminator Loss: 0.2014... Generator Loss: 3.3439
Epoch 1/1... Discriminator Loss: 0.2430... Generator Loss: 2.6729
Epoch 1/1... Discriminator Loss: 0.2564... Generator Loss: 2.9456
Epoch 1/1... Discriminator Loss: 0.1785... Generator Loss: 3.1544
Epoch 1/1... Discriminator Loss: 0.1546... Generator Loss: 2.6319
Epoch 1/1... Discriminator Loss: 0.1967... Generator Loss: 2.6102
Epoch 1/1... Discriminator Loss: 0.1517... Generator Loss: 3.1727
Epoch 1/1... Discriminator Loss: 0.1298... Generator Loss: 3.8249
Epoch 1/1... Discriminator Loss: 0.3535... Generator Loss: 1.9205
Epoch 1/1... Discriminator Loss: 0.4468... Generator Loss: 1.7771
Epoch 1/1... Discriminator Loss: 0.2144... Generator Loss: 3.1743
Epoch 1/1... Discriminator Loss: 0.4914... Generator Loss: 3.8512
Epoch 1/1... Discriminator Loss: 0.3473... Generator Loss: 2.0427
Epoch 1/1... Discriminator Loss: 0.4671... Generator Loss: 2.6377
Epoch 1/1... Discriminator Loss: 0.5710... Generator Loss: 2.8084
Epoch 1/1... Discriminator Loss: 0.3757... Generator Loss: 4.2022
Epoch 1/1... Discriminator Loss: 0.6748... Generator Loss: 1.3284
Epoch 1/1... Discriminator Loss: 0.4814... Generator Loss: 1.8459
Epoch 1/1... Discriminator Loss: 0.5260... Generator Loss: 2.1828
Epoch 1/1... Discriminator Loss: 0.3316... Generator Loss: 2.3111
Epoch 1/1... Discriminator Loss: 0.5711... Generator Loss: 1.6949
Epoch 1/1... Discriminator Loss: 0.5482... Generator Loss: 2.5522
Epoch 1/1... Discriminator Loss: 0.6746... Generator Loss: 3.1746
Epoch 1/1... Discriminator Loss: 0.6826... Generator Loss: 1.8750
Epoch 1/1... Discriminator Loss: 0.6254... Generator Loss: 2.0938
Epoch 1/1... Discriminator Loss: 0.9908... Generator Loss: 1.1152
Epoch 1/1... Discriminator Loss: 0.6884... Generator Loss: 1.9132
Epoch 1/1... Discriminator Loss: 1.0280... Generator Loss: 1.1205
Epoch 1/1... Discriminator Loss: 1.0768... Generator Loss: 2.1689
Epoch 1/1... Discriminator Loss: 1.1744... Generator Loss: 1.8775
Epoch 1/1... Discriminator Loss: 1.2572... Generator Loss: 1.3198
Epoch 1/1... Discriminator Loss: 0.8803... Generator Loss: 1.8091
Epoch 1/1... Discriminator Loss: 0.9992... Generator Loss: 1.0734
Epoch 1/1... Discriminator Loss: 0.8950... Generator Loss: 1.0009
Epoch 1/1... Discriminator Loss: 1.6037... Generator Loss: 2.5323
Epoch 1/1... Discriminator Loss: 1.1589... Generator Loss: 1.2702
Epoch 1/1... Discriminator Loss: 1.3295... Generator Loss: 1.6152
Epoch 1/1... Discriminator Loss: 0.9873... Generator Loss: 1.3447
Epoch 1/1... Discriminator Loss: 1.0720... Generator Loss: 1.7454
Epoch 1/1... Discriminator Loss: 0.9322... Generator Loss: 0.9365
Epoch 1/1... Discriminator Loss: 1.2047... Generator Loss: 1.3704
Epoch 1/1... Discriminator Loss: 1.5453... Generator Loss: 1.3571
Epoch 1/1... Discriminator Loss: 1.3851... Generator Loss: 0.9165
Epoch 1/1... Discriminator Loss: 0.9731... Generator Loss: 0.9817
Epoch 1/1... Discriminator Loss: 1.7407... Generator Loss: 0.6601
Epoch 1/1... Discriminator Loss: 1.5138... Generator Loss: 1.0995
Epoch 1/1... Discriminator Loss: 1.0657... Generator Loss: 1.1023
Epoch 1/1... Discriminator Loss: 1.0334... Generator Loss: 1.4972
Epoch 1/1... Discriminator Loss: 1.0990... Generator Loss: 0.8757
Epoch 1/1... Discriminator Loss: 0.8885... Generator Loss: 1.5736
Epoch 1/1... Discriminator Loss: 0.9533... Generator Loss: 1.1514
Epoch 1/1... Discriminator Loss: 1.1932... Generator Loss: 1.5752
Epoch 1/1... Discriminator Loss: 1.1024... Generator Loss: 1.2245
Epoch 1/1... Discriminator Loss: 0.7438... Generator Loss: 1.2896
Epoch 1/1... Discriminator Loss: 1.0089... Generator Loss: 1.2668
Epoch 1/1... Discriminator Loss: 0.8817... Generator Loss: 1.4725
Epoch 1/1... Discriminator Loss: 1.0933... Generator Loss: 1.2885
Epoch 1/1... Discriminator Loss: 0.8833... Generator Loss: 1.2467
Epoch 1/1... Discriminator Loss: 1.0716... Generator Loss: 1.3995
Epoch 1/1... Discriminator Loss: 1.0178... Generator Loss: 1.5501
Epoch 1/1... Discriminator Loss: 1.2016... Generator Loss: 1.3824
Epoch 1/1... Discriminator Loss: 0.9831... Generator Loss: 1.0078
Epoch 1/1... Discriminator Loss: 1.2644... Generator Loss: 1.4848
Epoch 1/1... Discriminator Loss: 1.0559... Generator Loss: 1.0882
Epoch 1/1... Discriminator Loss: 1.1481... Generator Loss: 1.1470
Epoch 1/1... Discriminator Loss: 1.6143... Generator Loss: 0.6497
Epoch 1/1... Discriminator Loss: 0.9610... Generator Loss: 1.2669
Epoch 1/1... Discriminator Loss: 1.2187... Generator Loss: 1.1982
Epoch 1/1... Discriminator Loss: 1.3185... Generator Loss: 1.1582
Epoch 1/1... Discriminator Loss: 0.8549... Generator Loss: 1.4039
Epoch 1/1... Discriminator Loss: 1.3180... Generator Loss: 1.1385
Epoch 1/1... Discriminator Loss: 1.2581... Generator Loss: 1.3650
Epoch 1/1... Discriminator Loss: 1.2883... Generator Loss: 0.6975
Epoch 1/1... Discriminator Loss: 0.9241... Generator Loss: 1.2415
Epoch 1/1... Discriminator Loss: 1.1038... Generator Loss: 1.1338
Epoch 1/1... Discriminator Loss: 0.9073... Generator Loss: 1.6930
Epoch 1/1... Discriminator Loss: 0.9482... Generator Loss: 1.7577
Epoch 1/1... Discriminator Loss: 0.7877... Generator Loss: 1.2368
Epoch 1/1... Discriminator Loss: 1.3967... Generator Loss: 1.4560
Epoch 1/1... Discriminator Loss: 1.1745... Generator Loss: 0.6133
Epoch 1/1... Discriminator Loss: 1.3692... Generator Loss: 0.9305
Epoch 1/1... Discriminator Loss: 1.0816... Generator Loss: 1.0296
Epoch 1/1... Discriminator Loss: 1.1683... Generator Loss: 0.9751
Epoch 1/1... Discriminator Loss: 1.0572... Generator Loss: 1.0956
Epoch 1/1... Discriminator Loss: 1.0112... Generator Loss: 1.2564
Epoch 1/1... Discriminator Loss: 1.1424... Generator Loss: 0.4999
Epoch 1/1... Discriminator Loss: 1.0391... Generator Loss: 1.5894
Epoch 1/1... Discriminator Loss: 1.0895... Generator Loss: 1.3796
Epoch 1/1... Discriminator Loss: 1.0836... Generator Loss: 1.5538
Epoch 1/1... Discriminator Loss: 0.8889... Generator Loss: 1.4279
Epoch 1/1... Discriminator Loss: 1.0983... Generator Loss: 1.1147
Epoch 1/1... Discriminator Loss: 1.0579... Generator Loss: 0.9422
Epoch 1/1... Discriminator Loss: 1.1289... Generator Loss: 1.3703
Epoch 1/1... Discriminator Loss: 1.2324... Generator Loss: 1.4263
Epoch 1/1... Discriminator Loss: 1.2814... Generator Loss: 0.4063
Epoch 1/1... Discriminator Loss: 1.0125... Generator Loss: 0.7991
Epoch 1/1... Discriminator Loss: 1.3836... Generator Loss: 1.5147
Epoch 1/1... Discriminator Loss: 0.9987... Generator Loss: 1.1663
Epoch 1/1... Discriminator Loss: 1.1094... Generator Loss: 1.0689
Epoch 1/1... Discriminator Loss: 1.4369... Generator Loss: 1.2777
Epoch 1/1... Discriminator Loss: 1.0100... Generator Loss: 1.0715
Epoch 1/1... Discriminator Loss: 1.2214... Generator Loss: 0.9514
Epoch 1/1... Discriminator Loss: 1.4617... Generator Loss: 0.7097
Epoch 1/1... Discriminator Loss: 1.1706... Generator Loss: 1.3857
Epoch 1/1... Discriminator Loss: 1.1100... Generator Loss: 1.0225
Epoch 1/1... Discriminator Loss: 1.1592... Generator Loss: 1.7167
Epoch 1/1... Discriminator Loss: 1.0824... Generator Loss: 1.2097
Epoch 1/1... Discriminator Loss: 1.1292... Generator Loss: 1.0511
Epoch 1/1... Discriminator Loss: 1.0899... Generator Loss: 0.7959
Epoch 1/1... Discriminator Loss: 1.0761... Generator Loss: 1.3325
Epoch 1/1... Discriminator Loss: 1.1582... Generator Loss: 1.3954
Epoch 1/1... Discriminator Loss: 1.3429... Generator Loss: 0.8881
Epoch 1/1... Discriminator Loss: 1.1758... Generator Loss: 0.9105
Epoch 1/1... Discriminator Loss: 1.2402... Generator Loss: 0.8988
Epoch 1/1... Discriminator Loss: 1.2166... Generator Loss: 0.9911
Epoch 1/1... Discriminator Loss: 1.1023... Generator Loss: 1.0410
Epoch 1/1... Discriminator Loss: 1.5834... Generator Loss: 0.7321
Epoch 1/1... Discriminator Loss: 1.0569... Generator Loss: 1.0669
Epoch 1/1... Discriminator Loss: 1.1075... Generator Loss: 1.5771
Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 1.2155
Epoch 1/1... Discriminator Loss: 1.0964... Generator Loss: 1.1428
Epoch 1/1... Discriminator Loss: 1.2254... Generator Loss: 0.8720
Epoch 1/1... Discriminator Loss: 1.0661... Generator Loss: 0.9414
Epoch 1/1... Discriminator Loss: 1.2886... Generator Loss: 1.5807
Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 1.0500
Epoch 1/1... Discriminator Loss: 1.1367... Generator Loss: 0.9397
Epoch 1/1... Discriminator Loss: 0.8004... Generator Loss: 0.8779
Epoch 1/1... Discriminator Loss: 1.0554... Generator Loss: 1.5496
Epoch 1/1... Discriminator Loss: 0.8650... Generator Loss: 1.2710
Epoch 1/1... Discriminator Loss: 1.5574... Generator Loss: 0.6127
Epoch 1/1... Discriminator Loss: 1.0523... Generator Loss: 0.9912
Epoch 1/1... Discriminator Loss: 1.1007... Generator Loss: 1.1417
Epoch 1/1... Discriminator Loss: 1.3208... Generator Loss: 1.1580
Epoch 1/1... Discriminator Loss: 0.8919... Generator Loss: 1.1224
Epoch 1/1... Discriminator Loss: 1.1055... Generator Loss: 1.2017
Epoch 1/1... Discriminator Loss: 1.3263... Generator Loss: 0.7115
Epoch 1/1... Discriminator Loss: 1.1295... Generator Loss: 1.4034
Epoch 1/1... Discriminator Loss: 1.2734... Generator Loss: 1.3046
Epoch 1/1... Discriminator Loss: 0.9436... Generator Loss: 1.1702
Epoch 1/1... Discriminator Loss: 1.3206... Generator Loss: 0.6224
Epoch 1/1... Discriminator Loss: 1.4728... Generator Loss: 0.5171
Epoch 1/1... Discriminator Loss: 1.2618... Generator Loss: 1.2090
Epoch 1/1... Discriminator Loss: 1.3065... Generator Loss: 0.6147
Epoch 1/1... Discriminator Loss: 1.0338... Generator Loss: 1.1523
Epoch 1/1... Discriminator Loss: 1.0354... Generator Loss: 0.8035
Epoch 1/1... Discriminator Loss: 1.3866... Generator Loss: 0.7417
Epoch 1/1... Discriminator Loss: 1.1899... Generator Loss: 1.1239
Epoch 1/1... Discriminator Loss: 1.1699... Generator Loss: 0.6689
Epoch 1/1... Discriminator Loss: 1.3311... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.4912... Generator Loss: 1.2922
Epoch 1/1... Discriminator Loss: 1.2530... Generator Loss: 0.8481
Epoch 1/1... Discriminator Loss: 1.2509... Generator Loss: 1.1811
Epoch 1/1... Discriminator Loss: 1.1304... Generator Loss: 0.6736
Epoch 1/1... Discriminator Loss: 1.4689... Generator Loss: 0.8551
Epoch 1/1... Discriminator Loss: 1.3429... Generator Loss: 1.1924
Epoch 1/1... Discriminator Loss: 1.1976... Generator Loss: 0.5991
Epoch 1/1... Discriminator Loss: 1.4784... Generator Loss: 0.6158
Epoch 1/1... Discriminator Loss: 0.8903... Generator Loss: 1.1115
Epoch 1/1... Discriminator Loss: 1.1278... Generator Loss: 0.9605
Epoch 1/1... Discriminator Loss: 1.3892... Generator Loss: 1.2209
Epoch 1/1... Discriminator Loss: 1.1691... Generator Loss: 0.8885
Epoch 1/1... Discriminator Loss: 1.3126... Generator Loss: 1.2075
Epoch 1/1... Discriminator Loss: 1.5004... Generator Loss: 0.5981
Epoch 1/1... Discriminator Loss: 1.4423... Generator Loss: 0.6347
Epoch 1/1... Discriminator Loss: 1.5261... Generator Loss: 0.8943
Epoch 1/1... Discriminator Loss: 0.9809... Generator Loss: 1.0418
Epoch 1/1... Discriminator Loss: 1.1477... Generator Loss: 1.1086
Epoch 1/1... Discriminator Loss: 1.0717... Generator Loss: 1.1292
Epoch 1/1... Discriminator Loss: 1.2534... Generator Loss: 0.9211
Epoch 1/1... Discriminator Loss: 1.3944... Generator Loss: 0.7396
Epoch 1/1... Discriminator Loss: 1.3016... Generator Loss: 0.7823
Epoch 1/1... Discriminator Loss: 1.2943... Generator Loss: 0.6744
Epoch 1/1... Discriminator Loss: 1.1319... Generator Loss: 1.2714
Epoch 1/1... Discriminator Loss: 1.1550... Generator Loss: 0.8775
Epoch 1/1... Discriminator Loss: 1.1919... Generator Loss: 1.2878
Epoch 1/1... Discriminator Loss: 1.2893... Generator Loss: 0.5768
Epoch 1/1... Discriminator Loss: 1.2171... Generator Loss: 0.7429
Epoch 1/1... Discriminator Loss: 1.3075... Generator Loss: 0.7752
Epoch 1/1... Discriminator Loss: 1.1481... Generator Loss: 1.2429
Epoch 1/1... Discriminator Loss: 1.3501... Generator Loss: 0.6445
Epoch 1/1... Discriminator Loss: 1.4571... Generator Loss: 0.7553
Epoch 1/1... Discriminator Loss: 1.5045... Generator Loss: 0.6512
Epoch 1/1... Discriminator Loss: 1.2719... Generator Loss: 0.9223
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 0.9321
Epoch 1/1... Discriminator Loss: 1.2819... Generator Loss: 1.0019
Epoch 1/1... Discriminator Loss: 0.8516... Generator Loss: 1.2838
Epoch 1/1... Discriminator Loss: 1.3056... Generator Loss: 0.7482
Epoch 1/1... Discriminator Loss: 1.0995... Generator Loss: 1.0233
Epoch 1/1... Discriminator Loss: 1.4527... Generator Loss: 0.6657
Epoch 1/1... Discriminator Loss: 1.0698... Generator Loss: 1.0973
Epoch 1/1... Discriminator Loss: 1.5427... Generator Loss: 0.7441
Epoch 1/1... Discriminator Loss: 1.5236... Generator Loss: 0.7471
Epoch 1/1... Discriminator Loss: 1.0314... Generator Loss: 0.9413
Epoch 1/1... Discriminator Loss: 1.3985... Generator Loss: 0.7980
Epoch 1/1... Discriminator Loss: 0.8341... Generator Loss: 1.3784
Epoch 1/1... Discriminator Loss: 1.3991... Generator Loss: 0.8153
Epoch 1/1... Discriminator Loss: 1.1309... Generator Loss: 0.8030
Epoch 1/1... Discriminator Loss: 1.1783... Generator Loss: 1.0420
Epoch 1/1... Discriminator Loss: 1.2969... Generator Loss: 0.8285
Epoch 1/1... Discriminator Loss: 0.9863... Generator Loss: 1.0085
Epoch 1/1... Discriminator Loss: 1.1281... Generator Loss: 1.1184
Epoch 1/1... Discriminator Loss: 1.4875... Generator Loss: 0.8318
Epoch 1/1... Discriminator Loss: 1.4420... Generator Loss: 0.7116
Epoch 1/1... Discriminator Loss: 1.2683... Generator Loss: 1.1365
Epoch 1/1... Discriminator Loss: 0.9379... Generator Loss: 1.0088
Epoch 1/1... Discriminator Loss: 1.4665... Generator Loss: 0.6566
Epoch 1/1... Discriminator Loss: 1.5469... Generator Loss: 0.9108
Epoch 1/1... Discriminator Loss: 1.1482... Generator Loss: 0.8733
Epoch 1/1... Discriminator Loss: 1.3408... Generator Loss: 0.5930
Epoch 1/1... Discriminator Loss: 1.3540... Generator Loss: 1.3402
Epoch 1/1... Discriminator Loss: 1.2529... Generator Loss: 0.8965
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.8223
Epoch 1/1... Discriminator Loss: 1.0548... Generator Loss: 0.7742
Epoch 1/1... Discriminator Loss: 1.3280... Generator Loss: 0.6880
Epoch 1/1... Discriminator Loss: 1.0848... Generator Loss: 0.6952
Epoch 1/1... Discriminator Loss: 1.0604... Generator Loss: 0.7647
Epoch 1/1... Discriminator Loss: 1.1215... Generator Loss: 0.8428
Epoch 1/1... Discriminator Loss: 1.3054... Generator Loss: 0.8372
Epoch 1/1... Discriminator Loss: 1.3395... Generator Loss: 0.5733
Epoch 1/1... Discriminator Loss: 0.8887... Generator Loss: 0.9968
Epoch 1/1... Discriminator Loss: 1.0886... Generator Loss: 0.7716
Epoch 1/1... Discriminator Loss: 1.4576... Generator Loss: 0.5774
Epoch 1/1... Discriminator Loss: 1.3361... Generator Loss: 0.6653
Epoch 1/1... Discriminator Loss: 1.0436... Generator Loss: 1.1298
Epoch 1/1... Discriminator Loss: 0.9974... Generator Loss: 1.1452
Epoch 1/1... Discriminator Loss: 0.9690... Generator Loss: 0.9019
Epoch 1/1... Discriminator Loss: 1.2333... Generator Loss: 0.8196
Epoch 1/1... Discriminator Loss: 1.3003... Generator Loss: 1.0074
Epoch 1/1... Discriminator Loss: 1.2101... Generator Loss: 0.8948
Epoch 1/1... Discriminator Loss: 1.4047... Generator Loss: 0.6421
Epoch 1/1... Discriminator Loss: 0.9460... Generator Loss: 0.8414
Epoch 1/1... Discriminator Loss: 1.1974... Generator Loss: 0.9121
Epoch 1/1... Discriminator Loss: 1.4066... Generator Loss: 1.1338
Epoch 1/1... Discriminator Loss: 1.5857... Generator Loss: 1.5798
Epoch 1/1... Discriminator Loss: 1.1594... Generator Loss: 1.1157
Epoch 1/1... Discriminator Loss: 1.4870... Generator Loss: 0.5231
Epoch 1/1... Discriminator Loss: 0.9924... Generator Loss: 1.1138
Epoch 1/1... Discriminator Loss: 1.1055... Generator Loss: 1.1484
Epoch 1/1... Discriminator Loss: 1.4733... Generator Loss: 0.6097
Epoch 1/1... Discriminator Loss: 1.2542... Generator Loss: 0.9910
Epoch 1/1... Discriminator Loss: 1.2398... Generator Loss: 0.7867
Epoch 1/1... Discriminator Loss: 1.3473... Generator Loss: 0.5777
Epoch 1/1... Discriminator Loss: 1.3292... Generator Loss: 0.7085
Epoch 1/1... Discriminator Loss: 1.2787... Generator Loss: 0.9598
Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.7075
Epoch 1/1... Discriminator Loss: 1.0608... Generator Loss: 0.7649
Epoch 1/1... Discriminator Loss: 1.0146... Generator Loss: 0.9529
Epoch 1/1... Discriminator Loss: 1.0770... Generator Loss: 1.0468
Epoch 1/1... Discriminator Loss: 0.9393... Generator Loss: 1.0867
Epoch 1/1... Discriminator Loss: 1.0058... Generator Loss: 0.9298
Epoch 1/1... Discriminator Loss: 1.0371... Generator Loss: 0.7177
Epoch 1/1... Discriminator Loss: 1.0229... Generator Loss: 1.1590
Epoch 1/1... Discriminator Loss: 1.2529... Generator Loss: 0.8954
Epoch 1/1... Discriminator Loss: 1.1696... Generator Loss: 0.8820
Epoch 1/1... Discriminator Loss: 1.2385... Generator Loss: 0.8849
Epoch 1/1... Discriminator Loss: 1.3609... Generator Loss: 1.0936
Epoch 1/1... Discriminator Loss: 1.2779... Generator Loss: 1.2824
Epoch 1/1... Discriminator Loss: 1.1985... Generator Loss: 1.1793
Epoch 1/1... Discriminator Loss: 0.9228... Generator Loss: 1.2039
Epoch 1/1... Discriminator Loss: 1.1364... Generator Loss: 0.9788
Epoch 1/1... Discriminator Loss: 1.2082... Generator Loss: 0.8420
Epoch 1/1... Discriminator Loss: 1.0822... Generator Loss: 1.2683
Epoch 1/1... Discriminator Loss: 1.3516... Generator Loss: 1.1656
Epoch 1/1... Discriminator Loss: 1.3282... Generator Loss: 0.8662
Epoch 1/1... Discriminator Loss: 1.4125... Generator Loss: 1.0309
Epoch 1/1... Discriminator Loss: 1.0278... Generator Loss: 1.0537
Epoch 1/1... Discriminator Loss: 0.9532... Generator Loss: 0.9223
Epoch 1/1... Discriminator Loss: 0.9448... Generator Loss: 1.0281
Epoch 1/1... Discriminator Loss: 1.2573... Generator Loss: 0.6694
Epoch 1/1... Discriminator Loss: 1.2356... Generator Loss: 1.1265
Epoch 1/1... Discriminator Loss: 1.0185... Generator Loss: 1.0521
Epoch 1/1... Discriminator Loss: 1.1929... Generator Loss: 0.7659
Epoch 1/1... Discriminator Loss: 1.0422... Generator Loss: 1.2631
Epoch 1/1... Discriminator Loss: 1.0272... Generator Loss: 1.1035
Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 1.0836
Epoch 1/1... Discriminator Loss: 1.1428... Generator Loss: 1.2482
Epoch 1/1... Discriminator Loss: 0.8168... Generator Loss: 0.8241
Epoch 1/1... Discriminator Loss: 1.2759... Generator Loss: 0.7321
Epoch 1/1... Discriminator Loss: 0.8762... Generator Loss: 1.1504
Epoch 1/1... Discriminator Loss: 0.8275... Generator Loss: 1.2327
Epoch 1/1... Discriminator Loss: 0.9053... Generator Loss: 2.0728
Epoch 1/1... Discriminator Loss: 1.0102... Generator Loss: 0.8400
Epoch 1/1... Discriminator Loss: 1.2760... Generator Loss: 0.7864
Epoch 1/1... Discriminator Loss: 1.2835... Generator Loss: 1.0391
Epoch 1/1... Discriminator Loss: 1.0260... Generator Loss: 0.8466
Epoch 1/1... Discriminator Loss: 0.8135... Generator Loss: 1.6333
Epoch 1/1... Discriminator Loss: 1.2723... Generator Loss: 0.6919
Epoch 1/1... Discriminator Loss: 1.3050... Generator Loss: 1.2795
Epoch 1/1... Discriminator Loss: 1.1013... Generator Loss: 0.9810
Epoch 1/1... Discriminator Loss: 1.4377... Generator Loss: 0.6368
Epoch 1/1... Discriminator Loss: 0.9150... Generator Loss: 0.7936
Epoch 1/1... Discriminator Loss: 1.0578... Generator Loss: 1.3736
Epoch 1/1... Discriminator Loss: 0.8633... Generator Loss: 1.0838
Epoch 1/1... Discriminator Loss: 0.8259... Generator Loss: 0.9305
Epoch 1/1... Discriminator Loss: 1.2824... Generator Loss: 1.2086
Epoch 1/1... Discriminator Loss: 1.0135... Generator Loss: 1.6539
Epoch 1/1... Discriminator Loss: 1.4942... Generator Loss: 0.6320
Epoch 1/1... Discriminator Loss: 0.9048... Generator Loss: 1.0831
Epoch 1/1... Discriminator Loss: 1.6768... Generator Loss: 0.6030
Epoch 1/1... Discriminator Loss: 1.3547... Generator Loss: 0.8776
Epoch 1/1... Discriminator Loss: 1.2568... Generator Loss: 0.7747
Epoch 1/1... Discriminator Loss: 1.2570... Generator Loss: 0.8312
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 1.1736
Epoch 1/1... Discriminator Loss: 1.3597... Generator Loss: 0.7767
Epoch 1/1... Discriminator Loss: 1.1479... Generator Loss: 1.0743
Epoch 1/1... Discriminator Loss: 0.7617... Generator Loss: 1.2965
Epoch 1/1... Discriminator Loss: 1.1062... Generator Loss: 0.8970
Epoch 1/1... Discriminator Loss: 1.1635... Generator Loss: 1.5937
Epoch 1/1... Discriminator Loss: 1.2926... Generator Loss: 0.8812

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.